Personalization

Online movie stream with mobile device. Man watching film on tablet with imaginary video player service.

Those of you who work in video advertising already know the technological shift taking place where the lines between TV and video blur. Even if you’ve already worked with analytics in this arena, it’s obviously a challenge to keep up with the latest technological changes.

The biggest area to explore now is advanced TV and all the options viewers use to watch content. Due to everything from connected TV to OTT apps, the way people watch content is more fragmented than ever. While some lament us not watching TV collectively in one place, it’s still imperative to gather granular data.

What kind of metrics should you scope out in each of these new TV sectors?

Behavioral Targeting

Trying to figure out viewer behavior is always a major hurdle, especially when people change their minds so often on what they want to see. For more advanced media advertising, though, you’ll have to dig even deeper into viewer behavior.

Using third-party data providers, you’ll be able to gain a wider picture of viewer behavior when taking from multiple sources like satellite TV, OTT (Over-the-Top) apps, or smart TV’s.

Reports are that 79% of advertisers and agencies plan to use advanced linear TV throughout the year for the above purposes. As a result, traditional TV advertising is on a major decline.

While you might think of Google and Facebook as the only ones into behavioral targeting, all other media companies are jumping onto the same boat. Out of all those, 56% of them are using behavioral targeting now for more significant ROI.

Areas to focus on here include purchase history and intent, general interest, web navigation history, and customer relationship management information. All of these behaviors can have varying data, depending on the type of media device being used. Using AI and machine learning for this aids in more accuracy.

Geographic Targeting

One thing you’ve likely tried to do is expand your audience on a geographic basis. With the world becoming smaller and other countries watching TV shows we only thought would stay in America or Europe, geographic targeting should become top of mind.

With addressable TV being another medium you’ll want to explore in extracting metrics, different geographic regions are perhaps watching the same live show. Using the addressable TV tactic, it’s possible to send different ads to targeted people collectively watching the same content.

You may want to target or limit ads to specific countries, states, postal codes, or DMAs (Designated Market Areas). Segmenting your analytics into regions like this helps you better understand the diversity of your audience and what their entertainment needs are.

Daypart Targeting

By limiting your ad targeting to specific parts of the day, you’re able to better target the people you know watch certain shows when at their most receptive. The targeting on this is always tricky, though, because advanced TV is so diverse.

Those watching at particular hours could end up bringing varied results considering some might watch at odd times, especially on mobile devices.

As Google points out, AI and machine learning are at the forefront of scoping out this type of ad targeting to better customize personalized marketing experiences. You’re now able to send ads to those watching in the evenings, overnight, or just on weekends.

Contextual and Technology Targeting

In these realms of targeting, you’ll work with relational metrics and broadcast ads based on how people watch shows. For contextual targeting, you’d base your ads on surrounding content from a webpage or a specific streaming device a viewer uses.

Technology targeting goes squarely on what device the person uses to watch content. Keep in mind this isn’t limited to device or service. It can also mean what browser they use or operating system.

Streaming media, from its onset, has been able to deliver captive audiences. It has also always had the ability to be used to target parameters built around your audience’s interest. Nothing much about the world of streaming media has changed except the quality and technological advancements that have made it better.

This conversation has less to do with streaming media and more to do with its sudden rise and culture-driven success. So what has changed to create this epic rise in usage? What is the impetus behind this culture shift that has empowered the growing popularity of streaming media?

Mobile Advertising Meets Streaming Media

Sometimes it just takes some time for the perfect relationship to develop. Mobile advertising isn’t necessarily new and streaming has been around for just as long, in fact, it isn’t as if these two are strangers. Streaming to mobile devices has been around long enough now that the technologies that supports streaming mobile options are as good as it gets.

One infographic reported an “87% increase in mobile advertising over the last five years.”

Advertising via mobile devices and using streaming media to fuel these efforts are now a practice that is becoming commonplace. This was the first step in the rise of popularity we are seeing in using streaming mobile advertising options.

The World Meets a New Generation

The “baby boomer” generation truly shouldered the advent of technology and the internet. Now it is time for the next generation to carry that forward. This is precisely a primary reason why we are witnessing a shift in how people are getting their information, where they are getting their entertainment and how they communicate.

How people are getting their information and news today, is often through a mobile device. Where people are getting information and entertainment is through more interest-based programming. How that information and entertainment is being transmitted is often through streaming media and more often everyday. This is the influence of Millennials and it is only beginning.

Although the biggest factor driving the growing popularity of streaming media is the generational demand, streaming media itself affords many benefits.

The Benefits of Streaming Media

In addition to being the ideal vehicle for delivering programmatic entertainment and information, there are many other reasons to use streaming media.

Affordable Advertising

The investment in traditional TV advertising is declining while more TV alternatives than ever before are appearing at a record pace. HBO Now, Hulu, Netflix, or YouTube are channels likely to be the most valuable to this fast growing contingent of “Content Connoisseurs.”

Building Brand Awareness

While the benefits of advertising at a fraction of the cost in comparison to traditional TV and radio advertising, streaming media also offers long-term benefits. Through the various streaming platforms, effectively using influence and building brand awareness has never been easier. Benefits like these continue to provide an ROI well beyond the numbers.

The Facts About Streaming Media

Speaking of numbers, there is plenty of support and reason based on the statistics to believe that, not only has streaming media arrived, it is here to stay. One recent story reported these findings…

“…programmatic video grew by an exponential 155 percent and now accounts for more than 45 percent of total online video ad spend.”

and…

“Mobile continues to be the “most” programmatic format, with 65 percent of mobile ad spend traded….”

Streaming video is how the next wave of consumers are going to be reached and it is going to be how traditional TV viewers eventually get their programming. Add to this the mobile-everything world we live in and it becomes evident, streaming media and mobile are the hottest new tandem in advertising. Contact us to find out more about this new media landscape and how streaming media and mobile can work together for you.

Artificial intelligence making possible new computer technologies and businesses

Understanding an audience and providing personalized content to them is an essential part of modern media delivery. Because of advancements in Artificial Intelligence (AI), media companies have every ability to deliver quality and personalized content to each and every member of their audience.

A.I Software or Predictive Analytics

People hear about artificial intelligence and wonder, what is that? According to the Forbe’s Primer on AI, current AI is so closely tied to machine learning that the two terms are used interchangeably. This means that artificial intelligence analyzes large amounts of data and changes the software algorithm to get a certain result. Thus predictive analytics is a core part of the development and continued use of artificial intelligence because the machine analyzes the data at hand and predicts changes needed to get results. This is key to self-driving cars, and it is key to behavioral analytics in media consumption and advertising.

Understanding Your Audience Through Behavioral Analytics

Human behavior is largely predictable when given enough information about a person’s habits and lifestyle. While we can always change our behavior, just think about the last 5 times you went to a coffee shop. Were they all different, or did you visit the same coffee shop 5 times? Based on just this tiny data set, someone could predict the likelihood that you would be at a certain coffee shop at a certain time tomorrow.

For media companies and advertising agencies, the use of OTT media to deliver content directly to their customers drives the motive behind predictive analytics software.

How OTT Provides More Analytical Information

OTT media is loosely defined as media delivered independently of a controlled distribution channel, like cable or satellite. Since individual customers are able to access OTT channels like Netflix or iTunes from anywhere and anytime, these channels provide a large amount of user-specific information to the channel owner.

What do people like to watch, listen to, or otherwise engage on a media platform? OTT analytics can accurately collect information relevant to each user, especially when combined with viewing profiles. This analytics is more useful to companies than any previous media consumption analysis. For example, a preschool age mom is likely to play children’s movies on a media platform during the day, but then switches her viewing to older content after 7 or 8 PM. Preferred time to consume media preferred device for certain media, and even smaller data sets all play a part in creating a behavioral analysis system.

As a data analysis platform is given more information, the predictive analysis gets more accurate and creates more specific recommendations for each customer. This provides companies a great base to retain and increase customer use of their platform.

Use AI to Retain Customers

It is easier and more affordable to keep a current customer engaged than it is to get a new customer. Predictive analytics software gives companies the ability to engage their customers, retaining them for long-term growth.

Customers who get content they want to watch are more likely to keep watching on that platform. This individualized access to content is a driving force that leads people to “cut the cord” and go completely OTT in their viewing habits. Anyone over 30 remembers the feeling of clicking the remote, and continuing to click it, hoping that something “good” will be on.

“100s of channels and nothing’s on.” If you know this feeling, you understand the power of analytically driven content to retain customers. Since each individual has different viewing preferences, a standardized product lineup will always drive some customers away while engaging other customers.

On the other hand, personalized content will help customers keep using the platform and consuming media. Because of personalized content on Netflix and Hulu, predictive analytics software and AI are quickly becoming essential parts of any content delivery platform. Even the traditional platforms are working to be more and more individualized to compete, so that cable subscribers get personalized content online.

Using A.I. technology can assist in social media analytics and audience personalization

There isn’t any denying that social media has become the major digital metropolis of our times. Twitter and Facebook alone make up a good portion of the world opining about virtually everything. In some cases, it also means setting precedents, something we’ve seen with the great organization in political groups.

Technology plays as much of a factor now in scoping out social media information. Machine learning can scan social channels to create preferences for specific types of media.

Take a look at how social media influences streaming media today and in setting content preferences for audiences.

How Does Social Media Opinion Influence Music Streaming?
Tech analysts continue to look at how much social media affects what you play on music streaming sites from Spotify to SoundCloud.

It’s not a new analysis when you consider the ability to create personal communities on social media was already around more than five years ago. Those options are even greater five years later, including the rise in media influencers and abilities to share information in real-time through live streaming apps.

All of this factors into one giant melting pot of opinion about music today. Trying to assess all that opinion, though, is impossible for even a large group of humans.

Only artificial intelligence and machine learning can tap into what’s said about music on social media and influence what you’ll hear on streaming music services.

Content Personalization on Music Streaming Services
If you’ve read up on machine learning recently, you’ve probably seen how much personalizing goes into entertainment and media. Thanks to A.I. programs being able to understand consumers better than they know themselves, more personalized recommendations can take place through media streaming.

In music streaming, you’re seeing this being used effectively at Pandora. The algorithms they use in machine learning work on a far deeper level beyond providing music recommendations from past behavior.

Their platform works by also extracting information about the emotions of songs and how they affect listeners. Known as the Music Genome Project, the Pandora team is made up of musicologists who understand the mechanics of music and how it resonates.

Using metadata from machine learning, Pandora can now provide more relevant music recommendations than any other service available. As eerie as this might sound, providing the music their users really want to hear is a powerful way to retain their user base.

Personalizing Streaming Video
Artificial intelligence works with streaming video as well. When machine learning programs scan social media, video companies can find out cumulative opinion on which TV shows or movies are being discussed. This can persuade leading streaming services like Netflix to decide which movies or TV shows they’ll promote over others.

Machine learning works equally on a more technical level particularly in preventing potential buffering lags. MIT Computer Science and Artificial Intelligence Lab (CSAIL) worked to create an AI program that prevents this from happening. They designed a neural network allowing a way to decide when a connection requires one particular algorithm over another.

It’s a way forward to prevent future buffering problems in video streaming services, which is good news as public demand increases.

The Future of More Targeted Marketing
Since streaming services are already mainstream and likely the main source of how we’ll view entertainment in the coming decade, machine learning is going to help with marketing. A.I. continues to evolve and help with this by working on social media channels. It results in clustering and knowing more detail about human behavior, bringing a way to mimic how specific demographics talk on social channels.

Creating a more personalized linguistic approach to streaming service marketing results in providing a truly customized experience no one’s seen before.

Visit our website to learn how we can use machine learning to scan social media and help influence what you provide in the way of streaming media.

In almost every industry today, you’re seeing an increase in personalized experiences for consumers. With more people wanting control over how they buy or consume content, entertainment companies are in the midst of abiding by these customer demands.

The consumer need to personalize content is a psychological impulse to find more control in a world filled with information overload. Since media content choices are often overwhelming, it’s all the more important for consumers to find something fitting their world views.

At the center of all this is artificial intelligence. Take a look at how machine learning continues to evolve personalized experiences in entertainment and media.

How Is A.I. Creating What You Want to See?
Most people know about how artificial intelligence operates in the financial services, healthcare, and manufacturing industries. In the entertainment world, it’s perhaps a little more discreet, though it’s actively being used.

Media analysts point out that A.I. is being used most actively in media companies at startup mode or in major conglomerates. In both cases, it’s important to use machine learning to find the proper demographics, and to keep dominance going in a more competitive media landscape.

Big company names like Spotify, Facebook, and Netflix are all focusing on using machine learning and using behavioral analytics to create content discovery.

Real Cases of Content Discovery
One of the most powerful methods toward personalization in entertainment is helping consumers find exactly what they initially didn’t think they wanted. These happy content discovery surprises are what’s going to change the way the entertainment industry works.

For smaller media companies who want audiences to find their work online, machine learning can help find an audience for everything. Statistics show tens of millions of people who create content end up having most of what they created never seen.

Underway now is creating machine learning programs that teach the model what people consider great content. Eventually, this could lead to millions of media sites being better sorted and presented as content discovery on various sites.

It could mean content written years ago finally being discovered and finding a proper audience.

Interaction Through Entertainment Systems
While content discovery is going to become a major development in machine learning, so too will interactions with numerous media devices.

The ability to make entertainment easily accessible at any time (and on any device) is already in development. Making it available on-demand through voice controls is also the next evolutionary leap. Amazon’s Echo is currently at the helm, including a major team-up with DISH last year to create a voice-activated DVR system.

Machine learning continues to work through Alexa to customize more than just a TV show or movie you prefer. It’s also going to help media companies know what kind of commercials or trailers you’d want to see.

Personalizing on this level might sound initially intrusive. Regardless, most consumers are likely to appreciate having a platform that makes consuming media more convenient with our schedules and viewing habits.

Interactive Participation in Entertainment
If more personalized interactions can take place on giving media consumers choice, what about having interactive participation in certain types of media?

We’ve already seen some of this in the social media realms thanks to live streaming apps. However, the possible integration of machine learning with virtual reality could create entirely new entertainment experiences.

Imagine being able to interact in a virtual reality space that’s customized based on your own personal preferences? Artificial intelligence is at a level now where it more or less knows you better than you know yourself.

Interacting in a customized entertainment landscape would become the ultimate escape, something many people may prefer in the times we live in.

Visit our website to learn more about how we can bring more personalized entertainment & media experiences to your customers.